# Installing libraries
library(TAM) # for Rasch modeling
library(WrightMap) # to build item-person (Wright) maps
library(lsr) #to calculate Cohen's d
library(DescTools) #to calculate etasquared
library(Rmisc) # to calculate summary statistics
library(tidyverse)
library(cowplot)
library(knitr)
library(magick)
# Create logo banner for plots
earthlab_orig <- image_read(path = "earth-lab-logo-white.png") %>%
image_scale("x80")
twitter_orig <- image_read(path = "plot-footer-twitter.png") %>%
image_scale("x70")
black_banner <- image_read(path = "black-banner.png")
earthlab_logo <- image_composite(image_scale(black_banner, "1000x100"), earthlab_orig, offset = "+30+10")
twitter_logo <- image_composite(image_scale(black_banner, "1000x100"), twitter_orig, offset = "+540+15")
logo <- image_append(image_scale(c(earthlab_logo, twitter_logo)), stack = FALSE)
logo
A total of 53 consenting participants provided demographic information related to gender, race & ethnicity through a series of items included on the pre-program survey instrument, administered prior to the start of the technical workshops.
##
## Call:
## lm(formula = Ability ~ Trial + Dimension + Cohort + Cohort *
## Dimension, data = abil_trial_dimension_all6)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.228 -1.128 0.032 0.982 5.022
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.75115 0.32182 8.549
## TrialBefore -2.93001 0.16283 -17.994
## DimensionData Science Practices -0.05436 0.44032 -0.123
## DimensionData Science Skills 0.16079 0.44032 0.365
## DimensionPython Skills 2.11823 0.44032 4.811
## DimensionScience Identity -0.12235 0.44032 -0.278
## CohortYear 2 -0.09295 0.46503 -0.200
## CohortYear 3 -0.13972 0.42259 -0.331
## DimensionData Science Practices:CohortYear 2 -0.24793 0.65766 -0.377
## DimensionData Science Skills:CohortYear 2 0.32802 0.66517 0.493
## DimensionPython Skills:CohortYear 2 0.72306 0.66517 1.087
## DimensionScience Identity:CohortYear 2 -0.59235 0.66517 -0.891
## DimensionData Science Practices:CohortYear 3 -0.12938 0.59763 -0.216
## DimensionData Science Skills:CohortYear 3 0.12335 0.60141 0.205
## DimensionPython Skills:CohortYear 3 0.70361 0.60141 1.170
## DimensionScience Identity:CohortYear 3 -0.62037 0.60141 -1.032
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## TrialBefore < 2e-16 ***
## DimensionData Science Practices 0.902
## DimensionData Science Skills 0.715
## DimensionPython Skills 2.05e-06 ***
## DimensionScience Identity 0.781
## CohortYear 2 0.842
## CohortYear 3 0.741
## DimensionData Science Practices:CohortYear 2 0.706
## DimensionData Science Skills:CohortYear 2 0.622
## DimensionPython Skills:CohortYear 2 0.278
## DimensionScience Identity:CohortYear 2 0.374
## DimensionData Science Practices:CohortYear 3 0.829
## DimensionData Science Skills:CohortYear 3 0.838
## DimensionPython Skills:CohortYear 3 0.243
## DimensionScience Identity:CohortYear 3 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.761 on 452 degrees of freedom
## Multiple R-squared: 0.5314, Adjusted R-squared: 0.5158
## F-statistic: 34.17 on 15 and 452 DF, p-value: < 2.2e-16
## Analysis of Variance Table
##
## Response: Ability
## Df Sum Sq Mean Sq F value Pr(>F)
## Trial 1 1004.44 1004.44 323.7867 <2e-16 ***
## Dimension 4 563.71 140.93 45.4291 <2e-16 ***
## Cohort 2 1.34 0.67 0.2158 0.8060
## Dimension:Cohort 8 20.59 2.57 0.8295 0.5769
## Residuals 452 1402.18 3.10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## eta.sq eta.sq.part
## Trial 0.3356796615 0.4173656492
## Dimension 0.1883110526 0.2866604141
## Cohort 0.0004473933 0.0009538292
## Dimension:Cohort 0.0068797627 0.0144690270
##
## Welch Two Sample t-test
##
## data: comfortpost$Ability and comfortpre$Ability
## t = 10.27, df = 87.882, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.950954 2.887157
## sample estimates:
## mean of x mean of y
## 2.415192354 -0.003863267
## [1] 2.096379
# science identity pre by year
ds_comms_pre1 <- filter(comfortpre, Cohort == "Year 1")
ds_comms_pre1
## Ability Dimension Cohort Trial
## 1 0.31876992 Data Science Communication Year 1 Before
## 2 -0.50742352 Data Science Communication Year 1 Before
## 3 1.77450789 Data Science Communication Year 1 Before
## 4 -0.37201079 Data Science Communication Year 1 Before
## 5 0.90986431 Data Science Communication Year 1 Before
## 6 0.60796709 Data Science Communication Year 1 Before
## 7 -0.50742352 Data Science Communication Year 1 Before
## 8 0.03831258 Data Science Communication Year 1 Before
## 9 0.03831258 Data Science Communication Year 1 Before
## 10 -0.09951847 Data Science Communication Year 1 Before
## 11 0.17763546 Data Science Communication Year 1 Before
## 12 2.46706048 Data Science Communication Year 1 Before
## 13 0.31876992 Data Science Communication Year 1 Before
## 14 0.46207106 Data Science Communication Year 1 Before
## 15 0.90986431 Data Science Communication Year 1 Before
## 16 0.75699922 Data Science Communication Year 1 Before
ds_comms_pre2 <- filter(comfortpre, Cohort == "Year 2")
ds_comms_pre2
## Ability Dimension Cohort Trial
## 1 0.03831258 Data Science Communication Year 2 Before
## 2 -0.50742352 Data Science Communication Year 2 Before
## 3 -1.65983969 Data Science Communication Year 2 Before
## 4 -1.65983969 Data Science Communication Year 2 Before
## 5 0.17763546 Data Science Communication Year 2 Before
## 6 0.90986431 Data Science Communication Year 2 Before
## 7 -0.23617675 Data Science Communication Year 2 Before
## 8 -1.34572147 Data Science Communication Year 2 Before
## 9 -0.50742352 Data Science Communication Year 2 Before
## 10 -0.23617675 Data Science Communication Year 2 Before
## 11 0.03831258 Data Science Communication Year 2 Before
## 12 1.58218261 Data Science Communication Year 2 Before
## 13 -0.50742352 Data Science Communication Year 2 Before
ds_comms_pre3 <- filter(comfortpre, Cohort == "Year 3")
ds_comms_pre3
## Ability Dimension Cohort Trial
## 1 -0.23617675 Data Science Communication Year 3 Before
## 2 0.03831258 Data Science Communication Year 3 Before
## 3 1.58218261 Data Science Communication Year 3 Before
## 4 -1.34572147 Data Science Communication Year 3 Before
## 5 0.03831258 Data Science Communication Year 3 Before
## 6 0.03831258 Data Science Communication Year 3 Before
## 7 0.46207106 Data Science Communication Year 3 Before
## 8 -0.09951847 Data Science Communication Year 3 Before
## 9 -0.23617675 Data Science Communication Year 3 Before
## 10 -0.50742352 Data Science Communication Year 3 Before
## 11 0.46207106 Data Science Communication Year 3 Before
## 12 0.17763546 Data Science Communication Year 3 Before
## 13 -0.37201079 Data Science Communication Year 3 Before
## 14 -1.19864112 Data Science Communication Year 3 Before
## 15 2.21085346 Data Science Communication Year 3 Before
## 16 -2.74709468 Data Science Communication Year 3 Before
## 17 -1.49876757 Data Science Communication Year 3 Before
## 18 -0.37201079 Data Science Communication Year 3 Before
## 19 0.03831258 Data Science Communication Year 3 Before
# science identity post by year
ds_comms_post1 <- filter(comfortpost, Cohort == "Year 1")
ds_comms_post1
## Ability Dimension Cohort Trial
## 1 1.7657308 Data Science Communication Year 1 After
## 2 1.7657308 Data Science Communication Year 1 After
## 3 3.8788828 Data Science Communication Year 1 After
## 4 1.0660680 Data Science Communication Year 1 After
## 5 3.0255702 Data Science Communication Year 1 After
## 6 2.1895153 Data Science Communication Year 1 After
## 7 0.7564803 Data Science Communication Year 1 After
## 8 2.4334785 Data Science Communication Year 1 After
## 9 1.3983095 Data Science Communication Year 1 After
## 10 1.5765678 Data Science Communication Year 1 After
## 11 1.3983095 Data Science Communication Year 1 After
## 12 3.0255702 Data Science Communication Year 1 After
## 13 1.5765678 Data Science Communication Year 1 After
## 14 1.5765678 Data Science Communication Year 1 After
## 15 3.4039561 Data Science Communication Year 1 After
## 16 3.0255702 Data Science Communication Year 1 After
ds_comms_post2 <- filter(comfortpost, Cohort == "Year 2")
ds_comms_post2
## Ability Dimension Cohort Trial
## 1 1.9687354 Data Science Communication Year 2 After
## 2 2.7083229 Data Science Communication Year 2 After
## 3 3.4039561 Data Science Communication Year 2 After
## 4 4.5387378 Data Science Communication Year 2 After
## 5 1.5765678 Data Science Communication Year 2 After
## 6 1.5765678 Data Science Communication Year 2 After
## 7 1.3983095 Data Science Communication Year 2 After
## 8 2.1895153 Data Science Communication Year 2 After
## 9 1.9687354 Data Science Communication Year 2 After
## 10 0.9090075 Data Science Communication Year 2 After
## 11 3.8788828 Data Science Communication Year 2 After
## 12 5.7939364 Data Science Communication Year 2 After
## 13 3.0255702 Data Science Communication Year 2 After
ds_comms_post3 <- filter(comfortpost, Cohort == "Year 3")
ds_comms_post3
## Ability Dimension Cohort Trial
## 1 2.4334785 Data Science Communication Year 3 After
## 2 1.5765678 Data Science Communication Year 3 After
## 3 5.7939364 Data Science Communication Year 3 After
## 4 1.9687354 Data Science Communication Year 3 After
## 5 1.9687354 Data Science Communication Year 3 After
## 6 1.7657308 Data Science Communication Year 3 After
## 7 1.2287191 Data Science Communication Year 3 After
## 8 2.1895153 Data Science Communication Year 3 After
## 9 0.7564803 Data Science Communication Year 3 After
## 10 5.7939364 Data Science Communication Year 3 After
## 11 1.2287191 Data Science Communication Year 3 After
## 12 1.5765678 Data Science Communication Year 3 After
## 13 3.8788828 Data Science Communication Year 3 After
## 14 0.4618885 Data Science Communication Year 3 After
## 15 3.8788828 Data Science Communication Year 3 After
## 16 3.4039561 Data Science Communication Year 3 After
## 17 1.7657308 Data Science Communication Year 3 After
## 18 2.4334785 Data Science Communication Year 3 After
## 19 3.0255702 Data Science Communication Year 3 After
# t-testing data science practices by year
# Year 1
t.test(ds_comms_post1$Ability, ds_comms_pre1$Ability)
##
## Welch Two Sample t-test
##
## data: ds_comms_post1$Ability and ds_comms_pre1$Ability
## t = 5.4751, df = 29.515, p-value = 6.412e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.040732 2.280408
## sample estimates:
## mean of x mean of y
## 2.1164297 0.4558599
cohensD(ds_comms_post1$Ability, ds_comms_pre1$Ability)
## [1] 1.93574
# Year 2
t.test(ds_comms_post2$Ability, ds_comms_pre2$Ability)
##
## Welch Two Sample t-test
##
## data: ds_comms_post2$Ability and ds_comms_pre2$Ability
## t = 6.3928, df = 20.827, p-value = 2.549e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.015839 3.961171
## sample estimates:
## mean of x mean of y
## 2.6874496 -0.3010552
cohensD(ds_comms_post2$Ability, ds_comms_pre2$Ability)
## [1] 2.507472
# Year 3
t.test(ds_comms_post3$Ability, ds_comms_pre3$Ability)
##
## Welch Two Sample t-test
##
## data: ds_comms_post3$Ability and ds_comms_pre3$Ability
## t = 6.2996, df = 32.67, p-value = 4.182e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.806121 3.530194
## sample estimates:
## mean of x mean of y
## 2.4805006 -0.1876567
cohensD(ds_comms_post3$Ability, ds_comms_pre3$Ability)
## [1] 2.043861
##
## Welch Two Sample t-test
##
## data: confpost$Ability and confpre$Ability
## t = 7.5006, df = 92.239, p-value = 3.837e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.531676 2.634901
## sample estimates:
## mean of x mean of y
## 2.074584526 -0.008704105
## [1] 1.531062
# science identity pre by year
ds_pract_pre1 <- filter(confpre, Cohort == "Year 1")
ds_pract_pre1
## Ability Dimension Cohort Trial
## 1 3.01420897 Data Science Practices Year 1 Before
## 2 0.13092222 Data Science Practices Year 1 Before
## 3 0.73619507 Data Science Practices Year 1 Before
## 4 -0.06921477 Data Science Practices Year 1 Before
## 5 1.59756681 Data Science Practices Year 1 Before
## 6 1.15428309 Data Science Practices Year 1 Before
## 7 -1.56252692 Data Science Practices Year 1 Before
## 8 -1.32768269 Data Science Practices Year 1 Before
## 9 -0.88813267 Data Science Practices Year 1 Before
## 10 0.73619507 Data Science Practices Year 1 Before
## 11 -0.47277589 Data Science Practices Year 1 Before
## 12 0.94296217 Data Science Practices Year 1 Before
## 13 0.94296217 Data Science Practices Year 1 Before
## 14 1.83444032 Data Science Practices Year 1 Before
## 15 0.33123446 Data Science Practices Year 1 Before
## 16 1.15428309 Data Science Practices Year 1 Before
ds_pract_pre2 <- filter(confpre, Cohort == "Year 2")
ds_pract_pre2
## Ability Dimension Cohort Trial
## 1 -0.88813267 Data Science Practices Year 2 Before
## 2 -0.06921477 Data Science Practices Year 2 Before
## 3 -0.88813267 Data Science Practices Year 2 Before
## 4 -2.38567830 Data Science Practices Year 2 Before
## 5 0.73619507 Data Science Practices Year 2 Before
## 6 -0.27011420 Data Science Practices Year 2 Before
## 7 1.59756681 Data Science Practices Year 2 Before
## 8 -3.77058166 Data Science Practices Year 2 Before
## 9 -0.67833205 Data Science Practices Year 2 Before
## 10 -0.27011420 Data Science Practices Year 2 Before
## 11 0.33123446 Data Science Practices Year 2 Before
## 12 0.13092222 Data Science Practices Year 2 Before
## 13 -1.56252692 Data Science Practices Year 2 Before
ds_pract_pre3 <- filter(confpre, Cohort == "Year 3")
ds_pract_pre3
## Ability Dimension Cohort Trial
## 1 1.1542831 Data Science Practices Year 3 Before
## 2 -0.6783320 Data Science Practices Year 3 Before
## 3 1.1542831 Data Science Practices Year 3 Before
## 4 -2.0836556 Data Science Practices Year 3 Before
## 5 0.3312345 Data Science Practices Year 3 Before
## 6 0.1309222 Data Science Practices Year 3 Before
## 7 1.1542831 Data Science Practices Year 3 Before
## 8 0.3312345 Data Science Practices Year 3 Before
## 9 -0.2701142 Data Science Practices Year 3 Before
## 10 0.5326596 Data Science Practices Year 3 Before
## 11 -0.2701142 Data Science Practices Year 3 Before
## 12 1.1542831 Data Science Practices Year 3 Before
## 13 2.6630033 Data Science Practices Year 3 Before
## 14 -0.8881327 Data Science Practices Year 3 Before
## 15 2.6630033 Data Science Practices Year 3 Before
## 16 -3.7705817 Data Science Practices Year 3 Before
## 17 -1.3276827 Data Science Practices Year 3 Before
## 18 -1.1038583 Data Science Practices Year 3 Before
## 19 -1.5625269 Data Science Practices Year 3 Before
# science identity post by year
ds_pract_post1 <- filter(confpost, Cohort == "Year 1")
ds_pract_post1
## Ability Dimension Cohort Trial
## 1 3.44744183 Data Science Practices Year 1 After
## 2 0.94296217 Data Science Practices Year 1 After
## 3 2.66300328 Data Science Practices Year 1 After
## 4 1.15428309 Data Science Practices Year 1 After
## 5 3.01420897 Data Science Practices Year 1 After
## 6 2.08638951 Data Science Practices Year 1 After
## 7 0.33123446 Data Science Practices Year 1 After
## 8 3.44744183 Data Science Practices Year 1 After
## 9 0.53265958 Data Science Practices Year 1 After
## 10 1.15428309 Data Science Practices Year 1 After
## 11 1.59756681 Data Science Practices Year 1 After
## 12 2.66300328 Data Science Practices Year 1 After
## 13 -0.06921477 Data Science Practices Year 1 After
## 14 2.08638951 Data Science Practices Year 1 After
## 15 2.66300328 Data Science Practices Year 1 After
## 16 3.44744183 Data Science Practices Year 1 After
ds_pract_post2 <- filter(confpost, Cohort == "Year 2")
ds_pract_post2
## Ability Dimension Cohort Trial
## 1 1.1542831 Data Science Practices Year 2 After
## 2 2.6630033 Data Science Practices Year 2 After
## 3 3.4474418 Data Science Practices Year 2 After
## 4 3.4474418 Data Science Practices Year 2 After
## 5 2.6630033 Data Science Practices Year 2 After
## 6 1.1542831 Data Science Practices Year 2 After
## 7 2.6630033 Data Science Practices Year 2 After
## 8 2.3593798 Data Science Practices Year 2 After
## 9 1.5975668 Data Science Practices Year 2 After
## 10 1.1542831 Data Science Practices Year 2 After
## 11 2.6630033 Data Science Practices Year 2 After
## 12 5.2408556 Data Science Practices Year 2 After
## 13 0.9429622 Data Science Practices Year 2 After
ds_pract_post3 <- filter(confpost, Cohort == "Year 3")
ds_pract_post3
## Ability Dimension Cohort Trial
## 1 3.4474418 Data Science Practices Year 3 After
## 2 1.1542831 Data Science Practices Year 3 After
## 3 5.2408556 Data Science Practices Year 3 After
## 4 1.1542831 Data Science Practices Year 3 After
## 5 1.3717866 Data Science Practices Year 3 After
## 6 3.0142090 Data Science Practices Year 3 After
## 7 1.1542831 Data Science Practices Year 3 After
## 8 2.0863895 Data Science Practices Year 3 After
## 9 0.5326596 Data Science Practices Year 3 After
## 10 3.0142090 Data Science Practices Year 3 After
## 11 0.7361951 Data Science Practices Year 3 After
## 12 2.6630033 Data Science Practices Year 3 After
## 13 -0.6783320 Data Science Practices Year 3 After
## 14 1.1542831 Data Science Practices Year 3 After
## 15 3.4474418 Data Science Practices Year 3 After
## 16 3.0142090 Data Science Practices Year 3 After
## 17 1.1542831 Data Science Practices Year 3 After
## 18 0.9429622 Data Science Practices Year 3 After
## 19 2.6630033 Data Science Practices Year 3 After
# t-testing data science practices by year
# Year 1
t.test(ds_pract_post1$Ability, ds_pract_pre1$Ability)
##
## Welch Two Sample t-test
##
## data: ds_pract_post1$Ability and ds_pract_pre1$Ability
## t = 3.4162, df = 29.98, p-value = 0.001844
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.5757861 2.2876111
## sample estimates:
## mean of x mean of y
## 1.9476311 0.5159325
cohensD(ds_pract_post1$Ability, ds_pract_pre1$Ability)
## [1] 1.207822
# Year 2
t.test(ds_pract_post2$Ability, ds_pract_pre2$Ability)
##
## Welch Two Sample t-test
##
## data: ds_pract_post2$Ability and ds_pract_pre2$Ability
## t = 5.8855, df = 23.661, p-value = 4.777e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.954043 4.067099
## sample estimates:
## mean of x mean of y
## 2.3961931 -0.6143776
cohensD(ds_pract_post2$Ability, ds_pract_pre2$Ability)
## [1] 2.308496
# Year 3
t.test(ds_pract_post3$Ability, ds_pract_pre3$Ability)
##
## Welch Two Sample t-test
##
## data: ds_pract_post3$Ability and ds_pract_pre3$Ability
## t = 4.1292, df = 35.364, p-value = 0.0002114
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.015828 2.979252
## sample estimates:
## mean of x mean of y
## 1.96144469 -0.03609519
cohensD(ds_pract_post3$Ability, ds_pract_pre3$Ability)
## [1] 1.339705
##
## Welch Two Sample t-test
##
## data: idpost$Ability and idpre$Ability
## t = 3.3174, df = 82.505, p-value = 0.001353
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6008267 2.4004040
## sample estimates:
## mean of x mean of y
## 1.43789668 -0.06271865
## [1] 0.6917187
# science identity pre by year
idpre1 <- filter(idpre, Cohort == "Year 1")
idpre1
## Ability Dimension Cohort Trial
## 1 1.8153924 Science Identity Year 1 Before
## 2 -1.2941506 Science Identity Year 1 Before
## 3 3.9602714 Science Identity Year 1 Before
## 4 0.4735946 Science Identity Year 1 Before
## 5 1.4568030 Science Identity Year 1 Before
## 6 1.1273724 Science Identity Year 1 Before
## 7 1.1273724 Science Identity Year 1 Before
## 8 -0.7836363 Science Identity Year 1 Before
## 9 -1.2941506 Science Identity Year 1 Before
## 10 2.2396942 Science Identity Year 1 Before
## 11 0.8055143 Science Identity Year 1 Before
## 12 1.8153924 Science Identity Year 1 Before
## 13 0.4735946 Science Identity Year 1 Before
## 14 0.4735946 Science Identity Year 1 Before
## 15 -0.3015740 Science Identity Year 1 Before
## 16 0.4735946 Science Identity Year 1 Before
idpre2 <- filter(idpre, Cohort == "Year 2")
idpre2
## Ability Dimension Cohort Trial
## 1 -0.7836363 Science Identity Year 2 Before
## 2 2.2396942 Science Identity Year 2 Before
## 3 -2.5847635 Science Identity Year 2 Before
## 4 -4.2749159 Science Identity Year 2 Before
## 5 -2.9679977 Science Identity Year 2 Before
## 6 -2.9679977 Science Identity Year 2 Before
## 7 -0.7836363 Science Identity Year 2 Before
## 8 -0.3015740 Science Identity Year 2 Before
## 9 -3.7820238 Science Identity Year 2 Before
## 10 3.9602714 Science Identity Year 2 Before
## 11 -0.7836363 Science Identity Year 2 Before
## 12 3.9602714 Science Identity Year 2 Before
idpre3 <- filter(idpre, Cohort == "Year 3")
idpre3
## Ability Dimension Cohort Trial
## 1 -0.7836363 Science Identity Year 3 Before
## 2 -0.7836363 Science Identity Year 3 Before
## 3 -0.7836363 Science Identity Year 3 Before
## 4 1.1273724 Science Identity Year 3 Before
## 5 0.8055143 Science Identity Year 3 Before
## 6 -1.7665849 Science Identity Year 3 Before
## 7 -3.7820238 Science Identity Year 3 Before
## 8 -3.3593292 Science Identity Year 3 Before
## 9 -2.1907468 Science Identity Year 3 Before
## 10 3.9602714 Science Identity Year 3 Before
## 11 -6.2108718 Science Identity Year 3 Before
## 12 2.8183989 Science Identity Year 3 Before
## 13 2.2396942 Science Identity Year 3 Before
## 14 0.1124107 Science Identity Year 3 Before
## 15 -3.7820238 Science Identity Year 3 Before
## 16 2.8183989 Science Identity Year 3 Before
## 17 3.9602714 Science Identity Year 3 Before
## 18 -0.7836363 Science Identity Year 3 Before
# science identity post by year
idpost1 <- filter(idpost, Cohort == "Year 1")
idpost1
## Ability Dimension Cohort Trial
## 1 2.2396950 Science Identity Year 1 After
## 2 -0.3015739 Science Identity Year 1 After
## 3 3.9602714 Science Identity Year 1 After
## 4 0.4735945 Science Identity Year 1 After
## 5 2.8183984 Science Identity Year 1 After
## 6 1.8153930 Science Identity Year 1 After
## 7 2.8183984 Science Identity Year 1 After
## 8 2.8183984 Science Identity Year 1 After
## 9 -0.7836289 Science Identity Year 1 After
## 10 2.2396950 Science Identity Year 1 After
## 11 0.8055138 Science Identity Year 1 After
## 12 2.2396950 Science Identity Year 1 After
## 13 0.1124107 Science Identity Year 1 After
## 14 0.4735945 Science Identity Year 1 After
## 15 1.1273719 Science Identity Year 1 After
## 16 1.8153930 Science Identity Year 1 After
idpost2 <- filter(idpost, Cohort == "Year 2")
idpost2
## Ability Dimension Cohort Trial
## 1 1.4568030 Science Identity Year 2 After
## 2 3.9602714 Science Identity Year 2 After
## 3 2.2396950 Science Identity Year 2 After
## 4 -1.2941469 Science Identity Year 2 After
## 5 -1.7665849 Science Identity Year 2 After
## 6 -0.7836289 Science Identity Year 2 After
## 7 3.9602714 Science Identity Year 2 After
## 8 1.8153930 Science Identity Year 2 After
## 9 2.2396950 Science Identity Year 2 After
## 10 3.9602714 Science Identity Year 2 After
## 11 0.8055138 Science Identity Year 2 After
## 12 3.9602714 Science Identity Year 2 After
idpost3 <- filter(idpost, Cohort == "Year 3")
idpost3
## Ability Dimension Cohort Trial
## 1 0.1124107 Science Identity Year 3 After
## 2 -0.7836289 Science Identity Year 3 After
## 3 -0.3015739 Science Identity Year 3 After
## 4 3.9602714 Science Identity Year 3 After
## 5 2.2396950 Science Identity Year 3 After
## 6 -1.7665849 Science Identity Year 3 After
## 7 -3.3593342 Science Identity Year 3 After
## 8 1.1273719 Science Identity Year 3 After
## 9 -0.3015739 Science Identity Year 3 After
## 10 3.9602714 Science Identity Year 3 After
## 11 1.4568030 Science Identity Year 3 After
## 12 2.8183984 Science Identity Year 3 After
## 13 2.2396950 Science Identity Year 3 After
## 14 0.4735945 Science Identity Year 3 After
## 15 0.8055138 Science Identity Year 3 After
## 16 2.8183984 Science Identity Year 3 After
## 17 3.9602714 Science Identity Year 3 After
## 18 1.4568030 Science Identity Year 3 After
# t-testing science identity by year
# Year 1
t.test(idpost1$Ability, idpre1$Ability)
##
## Welch Two Sample t-test
##
## data: idpost1$Ability and idpre1$Ability
## t = 1.5967, df = 29.977, p-value = 0.1208
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2111635 1.7241560
## sample estimates:
## mean of x mean of y
## 1.5420388 0.7855425
cohensD(idpost1$Ability, idpre1$Ability)
## [1] 0.5645036
# Year 2
t.test(idpost2$Ability, idpre2$Ability)
##
## Welch Two Sample t-test
##
## data: idpost2$Ability and idpre2$Ability
## t = 2.4198, df = 20.336, p-value = 0.025
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3428631 4.5944318
## sample estimates:
## mean of x mean of y
## 1.7128187 -0.7558287
cohensD(idpost2$Ability, idpre2$Ability)
## [1] 0.9878965
# Year 3
t.test(idpost3$Ability, idpre3$Ability)
##
## Welch Two Sample t-test
##
## data: idpost3$Ability and idpre3$Ability
## t = 1.8263, df = 30.586, p-value = 0.07758
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1780179 3.2114174
## sample estimates:
## mean of x mean of y
## 1.1620446 -0.3546552
cohensD(idpost3$Ability, idpre3$Ability)
## [1] 0.60876
# Year 3 vs. 1
t.test(idpost3$Ability, idpost1$Ability)
##
## Welch Two Sample t-test
##
## data: idpost3$Ability and idpost1$Ability
## t = -0.65286, df = 29.458, p-value = 0.5189
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.5696000 0.8096115
## sample estimates:
## mean of x mean of y
## 1.162045 1.542039
cohensD(idpost3$Ability, idpre1$Ability)
## [1] 0.2151985
# Looking at differences in growth across the three cohorts
sci_id_growth <- read.csv("sci-id-growth.csv")
sci_id_growth
## Cohort Diff
## 1 Year 1 0.424302606
## 2 Year 1 0.992576732
## 3 Year 1 -0.000000036
## 4 Year 1 -0.000000144
## 5 Year 1 1.361595374
## 6 Year 1 0.688020626
## 7 Year 1 1.691026016
## 8 Year 1 3.602034707
## 9 Year 1 0.510521677
## 10 Year 1 0.000000788
## 11 Year 1 -0.000000502
## 12 Year 1 0.424302606
## 13 Year 1 -0.361183961
## 14 Year 1 -0.000000144
## 15 Year 1 1.428945868
## 16 Year 1 1.341798371
## 17 Year 2 2.240439321
## 18 Year 2 1.720577168
## 19 Year 2 4.824458528
## 20 Year 2 2.980769085
## 21 Year 2 1.201412741
## 22 Year 2 2.184368750
## 23 Year 2 4.743907684
## 24 Year 2 2.116966986
## 25 Year 2 6.021718816
## 26 Year 2 -0.000000036
## 27 Year 2 1.589150093
## 28 Year 2 -0.000000036
## 29 Year 3 0.896046985
## 30 Year 3 0.000007380
## 31 Year 3 0.482062438
## 32 Year 3 2.832898993
## 33 Year 3 1.434180709
## 34 Year 3 0.000000001
## 35 Year 3 0.422689603
## 36 Year 3 4.486701108
## 37 Year 3 1.889172951
## 38 Year 3 -0.000000036
## 39 Year 3 7.667674865
## 40 Year 3 -0.000000490
## 41 Year 3 0.000000788
## 42 Year 3 0.361183817
## 43 Year 3 4.587537605
## 44 Year 3 -0.000000490
## 45 Year 3 -0.000000036
## 46 Year 3 2.240439321
sci_id_growth1 <- filter(sci_id_growth, Cohort == "Year 1")
sci_id_growth1
## Cohort Diff
## 1 Year 1 0.424302606
## 2 Year 1 0.992576732
## 3 Year 1 -0.000000036
## 4 Year 1 -0.000000144
## 5 Year 1 1.361595374
## 6 Year 1 0.688020626
## 7 Year 1 1.691026016
## 8 Year 1 3.602034707
## 9 Year 1 0.510521677
## 10 Year 1 0.000000788
## 11 Year 1 -0.000000502
## 12 Year 1 0.424302606
## 13 Year 1 -0.361183961
## 14 Year 1 -0.000000144
## 15 Year 1 1.428945868
## 16 Year 1 1.341798371
sci_id_growth2 <- filter(sci_id_growth, Cohort == "Year 2")
sci_id_growth2
## Cohort Diff
## 1 Year 2 2.240439321
## 2 Year 2 1.720577168
## 3 Year 2 4.824458528
## 4 Year 2 2.980769085
## 5 Year 2 1.201412741
## 6 Year 2 2.184368750
## 7 Year 2 4.743907684
## 8 Year 2 2.116966986
## 9 Year 2 6.021718816
## 10 Year 2 -0.000000036
## 11 Year 2 1.589150093
## 12 Year 2 -0.000000036
sci_id_growth3 <- filter(sci_id_growth, Cohort == "Year 3")
sci_id_growth3
## Cohort Diff
## 1 Year 3 0.896046985
## 2 Year 3 0.000007380
## 3 Year 3 0.482062438
## 4 Year 3 2.832898993
## 5 Year 3 1.434180709
## 6 Year 3 0.000000001
## 7 Year 3 0.422689603
## 8 Year 3 4.486701108
## 9 Year 3 1.889172951
## 10 Year 3 -0.000000036
## 11 Year 3 7.667674865
## 12 Year 3 -0.000000490
## 13 Year 3 0.000000788
## 14 Year 3 0.361183817
## 15 Year 3 4.587537605
## 16 Year 3 -0.000000490
## 17 Year 3 -0.000000036
## 18 Year 3 2.240439321
# t-testing for differences in growth
# Year 2 vs. Year 1
t.test(sci_id_growth2$Diff, sci_id_growth1$Diff)
##
## Welch Two Sample t-test
##
## data: sci_id_growth2$Diff and sci_id_growth1$Diff
## t = 2.8688, df = 15.509, p-value = 0.01141
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.4436788 2.9806235
## sample estimates:
## mean of x mean of y
## 2.4686474 0.7564963
# Year 3 vs. Year 1
t.test(sci_id_growth3$Diff, sci_id_growth1$Diff)
##
## Welch Two Sample t-test
##
## data: sci_id_growth3$Diff and sci_id_growth1$Diff
## t = 1.349, df = 24.446, p-value = 0.1897
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.401722 1.922129
## sample estimates:
## mean of x mean of y
## 1.5166998 0.7564963
cohensD(sci_id_growth3$Diff, sci_id_growth1$Diff)
## [1] 0.4455407
##
## Welch Two Sample t-test
##
## data: techconfpost$Ability and techconfpre$Ability
## t = 10.45, df = 89.127, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.431845 3.573682
## sample estimates:
## mean of x mean of y
## 3.0032369769 0.0004732148
## [1] 2.179052
# science identity pre by year
ds_skills_pre1 <- filter(techconfpre, Cohort == "Year 1")
ds_skills_pre1
## Ability Dimension Cohort Trial
## 1 1.78498502 Data Science Skills Year 1 Before
## 2 -2.89555118 Data Science Skills Year 1 Before
## 3 0.76338580 Data Science Skills Year 1 Before
## 4 0.19183423 Data Science Skills Year 1 Before
## 5 0.39200645 Data Science Skills Year 1 Before
## 6 -0.51889635 Data Science Skills Year 1 Before
## 7 0.58165000 Data Science Skills Year 1 Before
## 8 3.40743841 Data Science Skills Year 1 Before
## 9 0.39200645 Data Science Skills Year 1 Before
## 10 -0.02249625 Data Science Skills Year 1 Before
## 11 -0.25643576 Data Science Skills Year 1 Before
## 12 0.39200645 Data Science Skills Year 1 Before
## 13 -0.51889635 Data Science Skills Year 1 Before
## 14 0.39200645 Data Science Skills Year 1 Before
## 15 -0.02249625 Data Science Skills Year 1 Before
## 16 -0.82595605 Data Science Skills Year 1 Before
ds_skills_pre2 <- filter(techconfpre, Cohort == "Year 2")
ds_skills_pre2
## Ability Dimension Cohort Trial
## 1 0.5816500 Data Science Skills Year 2 Before
## 2 1.7849850 Data Science Skills Year 2 Before
## 3 -2.8955512 Data Science Skills Year 2 Before
## 4 -0.2564358 Data Science Skills Year 2 Before
## 5 1.7849850 Data Science Skills Year 2 Before
## 6 1.6156635 Data Science Skills Year 2 Before
## 7 0.1918342 Data Science Skills Year 2 Before
## 8 0.3920065 Data Science Skills Year 2 Before
## 9 1.1109643 Data Science Skills Year 2 Before
## 10 -0.8259560 Data Science Skills Year 2 Before
## 11 -1.7612494 Data Science Skills Year 2 Before
## 12 -0.8259560 Data Science Skills Year 2 Before
ds_skills_pre3 <- filter(techconfpre, Cohort == "Year 3")
ds_skills_pre3
## Ability Dimension Cohort Trial
## 1 0.7633858 Data Science Skills Year 3 Before
## 2 -2.8955512 Data Science Skills Year 3 Before
## 3 -0.5188964 Data Science Skills Year 3 Before
## 4 2.5077364 Data Science Skills Year 3 Before
## 5 -1.7612494 Data Science Skills Year 3 Before
## 6 0.7633858 Data Science Skills Year 3 Before
## 7 -0.5188964 Data Science Skills Year 3 Before
## 8 -0.8259560 Data Science Skills Year 3 Before
## 9 0.1918342 Data Science Skills Year 3 Before
## 10 1.6156635 Data Science Skills Year 3 Before
## 11 -2.8955512 Data Science Skills Year 3 Before
## 12 -0.2564358 Data Science Skills Year 3 Before
## 13 -2.8955512 Data Science Skills Year 3 Before
## 14 1.9571955 Data Science Skills Year 3 Before
## 15 0.3920065 Data Science Skills Year 3 Before
## 16 -0.5188964 Data Science Skills Year 3 Before
## 17 0.3920065 Data Science Skills Year 3 Before
## 18 0.3920065 Data Science Skills Year 3 Before
# science identity post by year
ds_skills_post1 <- filter(techconfpost, Cohort == "Year 1")
ds_skills_post1
## Ability Dimension Cohort Trial
## 1 3.4074384 Data Science Skills Year 1 After
## 2 4.8202632 Data Science Skills Year 1 After
## 3 2.7092572 Data Science Skills Year 1 After
## 4 1.9571955 Data Science Skills Year 1 After
## 5 0.3920064 Data Science Skills Year 1 After
## 6 3.1553096 Data Science Skills Year 1 After
## 7 2.1338550 Data Science Skills Year 1 After
## 8 4.0010730 Data Science Skills Year 1 After
## 9 2.5077364 Data Science Skills Year 1 After
## 10 4.3679136 Data Science Skills Year 1 After
## 11 2.9240041 Data Science Skills Year 1 After
## 12 2.7092572 Data Science Skills Year 1 After
## 13 0.7633858 Data Science Skills Year 1 After
## 14 2.5077364 Data Science Skills Year 1 After
## 15 2.9240041 Data Science Skills Year 1 After
## 16 1.7849850 Data Science Skills Year 1 After
ds_skills_post2 <- filter(techconfpost, Cohort == "Year 2")
ds_skills_post2
## Ability Dimension Cohort Trial
## 1 4.367914 Data Science Skills Year 2 After
## 2 4.001073 Data Science Skills Year 2 After
## 3 1.784985 Data Science Skills Year 2 After
## 4 4.820263 Data Science Skills Year 2 After
## 5 2.709257 Data Science Skills Year 2 After
## 6 3.686306 Data Science Skills Year 2 After
## 7 5.446170 Data Science Skills Year 2 After
## 8 2.133855 Data Science Skills Year 2 After
## 9 2.316706 Data Science Skills Year 2 After
## 10 1.110964 Data Science Skills Year 2 After
## 11 3.407438 Data Science Skills Year 2 After
## 12 3.686306 Data Science Skills Year 2 After
ds_skills_post3 <- filter(techconfpost, Cohort == "Year 3")
ds_skills_post3
## Ability Dimension Cohort Trial
## 1 1.957195 Data Science Skills Year 3 After
## 2 1.615663 Data Science Skills Year 3 After
## 3 2.924004 Data Science Skills Year 3 After
## 4 6.657984 Data Science Skills Year 3 After
## 5 1.615663 Data Science Skills Year 3 After
## 6 1.615663 Data Science Skills Year 3 After
## 7 1.110964 Data Science Skills Year 3 After
## 8 2.507736 Data Science Skills Year 3 After
## 9 3.407438 Data Science Skills Year 3 After
## 10 4.001073 Data Science Skills Year 3 After
## 11 4.367914 Data Science Skills Year 3 After
## 12 2.709257 Data Science Skills Year 3 After
## 13 2.507736 Data Science Skills Year 3 After
## 14 2.924004 Data Science Skills Year 3 After
## 15 4.367914 Data Science Skills Year 3 After
## 16 4.820263 Data Science Skills Year 3 After
## 17 2.133855 Data Science Skills Year 3 After
## 18 4.367914 Data Science Skills Year 3 After
# t-testing science identity by year
# Year 1
t.test(ds_skills_post1$Ability, ds_skills_pre1$Ability)
##
## Welch Two Sample t-test
##
## data: ds_skills_post1$Ability and ds_skills_pre1$Ability
## t = 5.6845, df = 29.684, p-value = 3.503e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.594562 3.384041
## sample estimates:
## mean of x mean of y
## 2.6915888 0.2022869
cohensD(ds_skills_post1$Ability, ds_skills_pre1$Ability)
## [1] 2.009757
# Year 2
t.test(ds_skills_post2$Ability, ds_skills_pre2$Ability)
##
## Welch Two Sample t-test
##
## data: ds_skills_post2$Ability and ds_skills_pre2$Ability
## t = 5.6842, df = 21.699, p-value = 1.077e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.040757 4.388293
## sample estimates:
## mean of x mean of y
## 3.289270 0.074745
cohensD(ds_skills_post2$Ability, ds_skills_pre2$Ability)
## [1] 2.320549
# Year 3
t.test(ds_skills_post3$Ability, ds_skills_pre3$Ability)
##
## Welch Two Sample t-test
##
## data: ds_skills_post3$Ability and ds_skills_pre3$Ability
## t = 6.558, df = 33.632, p-value = 1.725e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.28937 4.34663
## sample estimates:
## mean of x mean of y
## 3.0895691 -0.2284313
cohensD(ds_skills_post3$Ability, ds_skills_pre3$Ability)
## [1] 2.185985
##
## Welch Two Sample t-test
##
## data: techcomfortpost$Ability and techcomfortpre$Ability
## t = 15.426, df = 89.051, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.968754 6.437964
## sample estimates:
## mean of x mean of y
## 6.641079 0.937720
## [1] 3.216642
# science identity pre by year
python_pre1 <- filter(techcomfortpre, Cohort == "Year 1")
python_pre1
## Ability Dimension Cohort Trial
## 1 1.6668819 Python Skills Year 1 Before
## 2 2.8461207 Python Skills Year 1 Before
## 3 -0.1996241 Python Skills Year 1 Before
## 4 -0.1996241 Python Skills Year 1 Before
## 5 1.0266831 Python Skills Year 1 Before
## 6 -0.1996241 Python Skills Year 1 Before
## 7 5.9798356 Python Skills Year 1 Before
## 8 1.6668819 Python Skills Year 1 Before
## 9 -0.1996241 Python Skills Year 1 Before
## 10 -0.1996241 Python Skills Year 1 Before
## 11 -0.1996241 Python Skills Year 1 Before
## 12 -0.1996241 Python Skills Year 1 Before
## 13 -0.1996241 Python Skills Year 1 Before
## 14 1.0266831 Python Skills Year 1 Before
## 15 -0.1996241 Python Skills Year 1 Before
## 16 -0.1996241 Python Skills Year 1 Before
python_pre2 <- filter(techcomfortpre, Cohort == "Year 2")
python_pre2
## Ability Dimension Cohort Trial
## 1 3.1439720 Python Skills Year 2 Before
## 2 4.1577919 Python Skills Year 2 Before
## 3 -0.1996241 Python Skills Year 2 Before
## 4 1.0266831 Python Skills Year 2 Before
## 5 -0.1996241 Python Skills Year 2 Before
## 6 -0.1996241 Python Skills Year 2 Before
## 7 -0.1996241 Python Skills Year 2 Before
## 8 -0.1996241 Python Skills Year 2 Before
## 9 4.6112586 Python Skills Year 2 Before
## 10 2.5150264 Python Skills Year 2 Before
## 11 -0.1996241 Python Skills Year 2 Before
## 12 -0.1996241 Python Skills Year 2 Before
python_pre3 <- filter(techcomfortpre, Cohort == "Year 3")
python_pre3
## Ability Dimension Cohort Trial
## 1 3.6764147 Python Skills Year 3 Before
## 2 -0.1996241 Python Skills Year 3 Before
## 3 -0.1996241 Python Skills Year 3 Before
## 4 6.5363310 Python Skills Year 3 Before
## 5 -0.1996241 Python Skills Year 3 Before
## 6 4.1577919 Python Skills Year 3 Before
## 7 -0.1996241 Python Skills Year 3 Before
## 8 -0.1996241 Python Skills Year 3 Before
## 9 -0.1996241 Python Skills Year 3 Before
## 10 -0.1996241 Python Skills Year 3 Before
## 11 -0.1996241 Python Skills Year 3 Before
## 12 -0.1996241 Python Skills Year 3 Before
## 13 -0.1996241 Python Skills Year 3 Before
## 14 3.4186043 Python Skills Year 3 Before
## 15 -0.1996241 Python Skills Year 3 Before
## 16 -0.1996241 Python Skills Year 3 Before
## 17 -0.1996241 Python Skills Year 3 Before
## 18 1.6668819 Python Skills Year 3 Before
# science identity post by year
python_post1 <- filter(techcomfortpost, Cohort == "Year 1")
python_post1
## Ability Dimension Cohort Trial
## 1 7.217052 Python Skills Year 1 After
## 2 7.812953 Python Skills Year 1 After
## 3 5.950066 Python Skills Year 1 After
## 4 6.141463 Python Skills Year 1 After
## 5 6.748451 Python Skills Year 1 After
## 6 5.950066 Python Skills Year 1 After
## 7 7.217052 Python Skills Year 1 After
## 8 4.580920 Python Skills Year 1 After
## 9 6.748451 Python Skills Year 1 After
## 10 6.141463 Python Skills Year 1 After
## 11 5.760429 Python Skills Year 1 After
## 12 4.366900 Python Skills Year 1 After
## 13 5.380136 Python Skills Year 1 After
## 14 5.760429 Python Skills Year 1 After
## 15 5.186832 Python Skills Year 1 After
## 16 5.760429 Python Skills Year 1 After
python_post2 <- filter(techcomfortpost, Cohort == "Year 2")
python_post2
## Ability Dimension Cohort Trial
## 1 7.491356 Python Skills Year 2 After
## 2 9.952048 Python Skills Year 2 After
## 3 4.788244 Python Skills Year 2 After
## 4 7.491356 Python Skills Year 2 After
## 5 6.748451 Python Skills Year 2 After
## 6 6.336577 Python Skills Year 2 After
## 7 9.952048 Python Skills Year 2 After
## 8 5.186832 Python Skills Year 2 After
## 9 6.972738 Python Skills Year 2 After
## 10 4.788244 Python Skills Year 2 After
## 11 5.570921 Python Skills Year 2 After
## 12 7.491356 Python Skills Year 2 After
python_post3 <- filter(techcomfortpost, Cohort == "Year 3")
python_post3
## Ability Dimension Cohort Trial
## 1 4.989882 Python Skills Year 3 After
## 2 5.186832 Python Skills Year 3 After
## 3 6.141463 Python Skills Year 3 After
## 4 9.952048 Python Skills Year 3 After
## 5 4.989882 Python Skills Year 3 After
## 6 5.380136 Python Skills Year 3 After
## 7 3.914069 Python Skills Year 3 After
## 8 6.141463 Python Skills Year 3 After
## 9 7.812953 Python Skills Year 3 After
## 10 9.952048 Python Skills Year 3 After
## 11 9.952048 Python Skills Year 3 After
## 12 7.812953 Python Skills Year 3 After
## 13 7.217052 Python Skills Year 3 After
## 14 7.491356 Python Skills Year 3 After
## 15 6.748451 Python Skills Year 3 After
## 16 9.952048 Python Skills Year 3 After
## 17 4.145073 Python Skills Year 3 After
## 18 8.216612 Python Skills Year 3 After
# t-testing science identity by year
# Year 1
t.test(python_post1$Ability, python_pre1$Ability)
##
## Welch Two Sample t-test
##
## data: python_post1$Ability and python_pre1$Ability
## t = 10.946, df = 23.525, p-value = 1.036e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.284734 6.278547
## sample estimates:
## mean of x mean of y
## 6.0451932 0.7635529
cohensD(python_post1$Ability, python_pre1$Ability)
## [1] 3.870099
# Year 2
t.test(python_post2$Ability, python_pre2$Ability)
##
## Welch Two Sample t-test
##
## data: python_post2$Ability and python_pre2$Ability
## t = 7.6856, df = 21.854, p-value = 1.193e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.180347 7.271787
## sample estimates:
## mean of x mean of y
## 6.897514 1.171447
cohensD(python_post2$Ability, python_pre2$Ability)
## [1] 3.137618
# Year 3
t.test(python_post3$Ability, python_pre3$Ability)
##
## Welch Two Sample t-test
##
## data: python_post3$Ability and python_pre3$Ability
## t = 8.8424, df = 33.996, p-value = 2.471e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.669601 7.456560
## sample estimates:
## mean of x mean of y
## 6.9997981 0.9367173
cohensD(python_post3$Ability, python_pre3$Ability)
## [1] 2.94747
# Year 3 vs. 1 (PRE)
t.test(python_pre3$Ability, python_pre1$Ability)
##
## Welch Two Sample t-test
##
## data: python_pre3$Ability and python_pre1$Ability
## t = 0.26877, df = 31.783, p-value = 0.7898
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.139565 1.485894
## sample estimates:
## mean of x mean of y
## 0.9367173 0.7635529
t.test(python_pre2$Ability, python_pre1$Ability)
##
## Welch Two Sample t-test
##
## data: python_pre2$Ability and python_pre1$Ability
## t = 0.59017, df = 22.169, p-value = 0.561
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.024813 1.840601
## sample estimates:
## mean of x mean of y
## 1.1714470 0.7635529
cohensD(idpost3$Ability, idpre1$Ability)
## [1] 0.2151985